提升地下缺陷检测中微弱信号的识别能力，解决低信噪比下的漏检问题。
A Weak-Signal-Aware Framework for Subsurface Defect Detection: Mechanisms for Enhancing Low-SCR Hyperbolic Signatures
- 通过部分卷积和异质分组注意力，增强微弱信号并抑制杂波。
- 在RTST数据集上达到0.6958 [email protected]，推理速度达164 FPS。
- 适合基础设施无损检测场景，尤其关注低信噪比下的精准定位。
基于探地雷达的地下缺陷检测面临微弱信号挑战：衍射双曲线信号强度低、信噪比差、波场相似性高且几何形态退化。现有轻量级检测器侧重效率而忽略敏感性，难以保留低频结构或分离异质杂波。本文提出WSA-Net框架，通过物理特征重建强化微弱信号。其整合四项机制：利用部分卷积实现信号保持；通过异质分组注意力抑制杂波；几何重构锐化双曲线弧线；上下文锚定化解语义模糊。在RTST数据集上的评估显示，WSA-Net实现0.6958 [email protected]与164 FPS推理速度，仅需2.412 M参数。结果证明，在轻量架构中引入以信号为中心的感知机制，可有效降低基础设施检测中的漏检率。
原文摘要 · Abstract (English)
Subsurface defect detection via Ground Penetrating Radar is challenged by "weak signals" faint diffraction hyperbolas with low signal-to-clutter ratios, high wavefield similarity, and geometric degradation. Existing lightweight detectors prioritize efficiency over sensitivity, failing to preserve low-frequency structures or decouple heterogeneous clutter. We propose WSA-Net, a framework designed to enhance faint signatures through physical-feature reconstruction. Moving beyond simple parameter reduction, WSA-Net integrates four mechanisms: Signal preservation using partial convolutions; Clutter suppression via heterogeneous grouping attention; Geometric reconstruction to sharpen hyperbolic arcs; Context anchoring to resolve semantic ambiguities. Evaluations on the RTSTdataset show WSA-Net achieves 0.6958 [email protected] and 164 FPS with only 2.412 M parameters. Results prove that signal-centric awareness in lightweight architectures effectively reduces false negatives in infrastructure inspection.
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